Take a different approach with metabolites:

  1. ID metabolites responding to microbes
  2. Ask if those are associated with leaf length

Here include genotype for the treatment models but use scaled and centered values for leaf associations.

NOTE: THIS IS THE FINAL ANALYSIS. TRIED AND DID NOT LIKE LME MODELS. TOO MANY SINGULARITIES. I THINK THERE IS NOT ENOUGH DATA TO ESTIMATE BLOCK EFFECCTS WELL.

NOTE: NEXT STEPS: TRY MAKING TERNARY PLOT WITH SIMPLE MEANS.

library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.6     ✓ dplyr   1.0.8
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(broom)

get leaflength data

leaflength <- read_csv("../../plant/output/leaf_lengths_metabolite.csv") %>%
  mutate(pot=str_pad(pot, width=3, pad="0"),
         sampleID=str_c("wyo", genotype, pot, sep="_")) %>%
  select(sampleID, genotype, trt, leaf_avg_std)
Rows: 36 Columns: 6
── Column specification ─────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): soil, genotype, trt
dbl (3): pot, leaf_avg, leaf_avg_std

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
leaflength %>% arrange(sampleID)

get and wrangle metabolite data

met_raw <-read_csv("../input/metabolites_set1.csv")
Rows: 72 Columns: 671
── Column specification ─────────────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): tissue, soil, genotype, autoclave, time_point, concatenate
dbl (665): submission_number, pot, sample_mass mg, xylulose NIST, xylose, xylonic aci...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
met <- met_raw %>% 
  mutate(pot=str_pad(pot, width = 3, pad = "0")) %>%
  mutate(sampleID=str_c("wyo", genotype, pot, sep="_")) %>%
  mutate(trt=ifelse(is.na(autoclave), "BLANK", autoclave)) %>%
  select(sampleID, genotype, tissue, trt, sample_mass = `sample_mass mg`, !submission_number:concatenate)  %>%
  
  #bring in leaf length
  left_join({leaflength %>% select(sampleID, leaf_avg_std)}) %>%
  select(sampleID, genotype, tissue, trt, leaf_avg_std, everything()) %>%
  
  #make long
  pivot_longer(!sampleID:sample_mass, names_to = "metabolite", values_to = "met_amt") %>%
  
  #filter away unnamed
  filter(str_detect(metabolite, pattern="^[0-9]+$", negate=TRUE)) %>%
  
  #adjust by sample mass
  mutate(met_per_mg=met_amt/sample_mass) %>%
  
  pivot_wider(id_cols = c(sampleID, genotype, trt, leaf_avg_std), 
              names_from = c(tissue, metabolite), 
              values_from = starts_with("met_"),
              names_sep = "_")
Joining, by = "sampleID"
met 

split this into two data frames, one normalized by tissue amount and one not.

met_per_mg <- met %>% select(sampleID, genotype, trt, leaf_avg_std, starts_with("met_per_mg")) %>%
  as.data.frame() %>% column_to_rownames("sampleID")
met_amt <- met %>% select(sampleID,  genotype, trt, leaf_avg_std, starts_with("met_amt")) %>%
  as.data.frame() %>% column_to_rownames("sampleID")

find sig metabolites

Live vs blank dead

normalized

met_per_mg_lm_results_trt <- met_per_mg_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
  select(-`p.value_trt_(Intercept)`)

met_per_mg_lm_results_leaf <- met_per_mg_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
  select(-`p.value_leaf_(Intercept)`)

met_per_mg_lm_results <- inner_join(met_per_mg_lm_results_trt, met_per_mg_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
  group_by(tissue) %>%
  mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
  rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
  ungroup()
Joining, by = "metabolite"
met_per_mg_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())

met_per_mg_lm_results %>% 
  mutate(both=(p.value_trt_trtlive < 0.1) & (p.value_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("p.value"), ~ sum(.x < .05)), both=sum(both), count=n())

met_per_mg_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)



met_per_mg_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv("../output/met_per_mg_lm_full.csv")

raw

met_amt_lm <- met_amt %>%
  mutate(trt=ifelse(str_detect(trt, "live"), "live", "blank_dead")) %>%
  pivot_longer(cols=starts_with("met"), names_to = "metabolite") %>%
  mutate(tissue=str_extract(metabolite, "leaf|root")) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))
met_amt_lm_results_trt <- met_amt_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
  select(-`p.value_trt_(Intercept)`)

met_amt_lm_results_leaf <- met_amt_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
  select(-`p.value_leaf_(Intercept)`)

met_amt_lm_results <- inner_join(met_amt_lm_results_trt, met_amt_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
  group_by(tissue) %>%
  mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
  rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
  ungroup()
Joining, by = "metabolite"
met_amt_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())

met_amt_lm_results %>% 
  mutate(both=(p.value_trt_trtlive < 0.1) & (p.value_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("p.value"), ~ sum(.x < .05)), both=sum(both), count=n())

met_amt_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)

met_amt_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv("../output/met_amt_lm_full.csv")

NO BLANK

find sig metabolites

Live vs dead

normalized

```r
met_per_mg_lm <- met_per_mg %>%
  filter(trt != \BLANK\) %>% 
  mutate(trt=ifelse(str_detect(trt, \live\), \live\, \blank_dead\)) %>%
  pivot_longer(cols=starts_with(\met\), names_to = \metabolite\) %>%
  mutate(tissue=str_extract(metabolite, \leaf|root\)) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))

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```r
```r
met_per_mg_lm_results_trt <- met_per_mg_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \trt_\) %>%
  select(-`p.value_trt_(Intercept)`)

met_per_mg_lm_results_leaf <- met_per_mg_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \leaf_\) %>%
  select(-`p.value_leaf_(Intercept)`)

met_per_mg_lm_results <- inner_join(met_per_mg_lm_results_trt, met_per_mg_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,\(leaf|root)\)) %>%
  group_by(tissue) %>%
  mutate(across(starts_with(\p.value\), .fns = p.adjust, method = \fdr\, .names = \FDR_{.col}\)) %>%
  rename_with(~ str_replace(.x, \FDR_p.value\, \FDR\)) %>%
  ungroup()

<!-- rnb-source-end -->

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Joining, by =




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```r
```r
met_per_mg_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with(\FDR\), ~ sum(.x < .1)), both=sum(both), count=n())

met_per_mg_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)

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```r
```r

met_per_mg_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv(\../output/met_per_mg_lm_full_NOBLANK.csv\)

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raw

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```r
```r
met_amt_lm <- met_amt %>%
  filter(trt != \BLANK\) %>% 
  pivot_longer(cols=starts_with(\met\), names_to = \metabolite\) %>%
  mutate(tissue=str_extract(metabolite, \leaf|root\)) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))

<!-- rnb-source-end -->

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```r
```r
met_amt_lm_results_trt <- met_amt_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \trt_\) %>%
  select(-`p.value_trt_(Intercept)`)

met_amt_lm_results_leaf <- met_amt_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = \leaf_\) %>%
  select(-`p.value_leaf_(Intercept)`)

met_amt_lm_results <- inner_join(met_amt_lm_results_trt, met_amt_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,\(leaf|root)\)) %>%
  group_by(tissue) %>%
  mutate(across(starts_with(\p.value\), .fns = p.adjust, method = \fdr\, .names = \FDR_{.col}\)) %>%
  rename_with(~ str_replace(.x, \FDR_p.value\, \FDR\)) %>%
  ungroup()

<!-- rnb-source-end -->

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Joining, by =




<!-- rnb-output-end -->

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```r
```r
met_amt_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with(\FDR\), ~ sum(.x < .1)), both=sum(both), count=n())

met_amt_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)

<!-- rnb-source-end -->

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<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["tissue"],"name":[1],"type":["chr"],"align":["left"]},{"label":["FDR_trt_trtlive"],"name":[2],"type":["int"],"align":["right"]},{"label":["FDR_trt_genotypeR500"],"name":[3],"type":["int"],"align":["right"]},{"label":["FDR_trt_trtlive:genotypeR500"],"name":[4],"type":["int"],"align":["right"]},{"label":["FDR_leaf_value_scale"],"name":[5],"type":["int"],"align":["right"]},{"label":["both"],"name":[6],"type":["int"],"align":["right"]},{"label":["count"],"name":[7],"type":["int"],"align":["right"]}],"data":[{"1":"leaf","2":"0","3":"65","4":"0","5":"3","6":"0","7":"176"},{"1":"root","2":"22","3":"33","4":"1","5":"0","6":"0","7":"176"}],"options":{"columns":{"min":{},"max":[10],"total":[7]},"rows":{"min":[10],"max":[10],"total":[2]},"pages":{}}}
  </script>
</div>

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```r
```r

met_amt_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv(\../output/met_amt_lm_full_NOBLANK.csv\)

```

---
title: "Metabolites...Back to basics"
author: "Julin Maloof"
date: "02/22/2021"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

Take a different approach with metabolites:

1) ID metabolites responding to microbes
2) Ask if those are associated with leaf length

Here include genotype for the treatment models but use scaled and centered values for leaf associations.

NOTE: THIS IS THE FINAL ANALYSIS. TRIED AND DID NOT LIKE LME MODELS.  TOO MANY SINGULARITIES.  I THINK THERE IS NOT ENOUGH DATA TO ESTIMATE BLOCK EFFECCTS WELL.

NOTE: NEXT STEPS:  TRY MAKING TERNARY PLOT WITH SIMPLE MEANS.


```{r}
library(tidyverse)
library(broom)
```

get leaflength data
```{r}
leaflength <- read_csv("../../plant/output/leaf_lengths_metabolite.csv") %>%
  mutate(pot=str_pad(pot, width=3, pad="0"),
         sampleID=str_c("wyo", genotype, pot, sep="_")) %>%
  select(sampleID, genotype, trt, leaf_avg_std)
leaflength %>% arrange(sampleID)
```

get and wrangle metabolite data
```{r}
met_raw <-read_csv("../input/metabolites_set1.csv")
met <- met_raw %>% 
  mutate(pot=str_pad(pot, width = 3, pad = "0")) %>%
  mutate(sampleID=str_c("wyo", genotype, pot, sep="_")) %>%
  mutate(trt=ifelse(is.na(autoclave), "BLANK", autoclave)) %>%
  select(sampleID, genotype, tissue, trt, sample_mass = `sample_mass mg`, !submission_number:concatenate)  %>%
  
  #bring in leaf length
  left_join({leaflength %>% select(sampleID, leaf_avg_std)}) %>%
  select(sampleID, genotype, tissue, trt, leaf_avg_std, everything()) %>%
  
  #make long
  pivot_longer(!sampleID:sample_mass, names_to = "metabolite", values_to = "met_amt") %>%
  
  #filter away unnamed
  filter(str_detect(metabolite, pattern="^[0-9]+$", negate=TRUE)) %>%
  
  #adjust by sample mass
  mutate(met_per_mg=met_amt/sample_mass) %>%
  
  pivot_wider(id_cols = c(sampleID, genotype, trt, leaf_avg_std), 
              names_from = c(tissue, metabolite), 
              values_from = starts_with("met_"),
              names_sep = "_")

met 
```

split this into two data frames, one normalized by tissue amount and one not.
```{r}
met_per_mg <- met %>% select(sampleID, genotype, trt, leaf_avg_std, starts_with("met_per_mg")) %>%
  as.data.frame() %>% column_to_rownames("sampleID")
met_amt <- met %>% select(sampleID,  genotype, trt, leaf_avg_std, starts_with("met_amt")) %>%
  as.data.frame() %>% column_to_rownames("sampleID")
```


## find sig metabolites

Live vs blank dead

normalized
```{r}
met_per_mg_lm <- met_per_mg %>%
  mutate(trt=ifelse(str_detect(trt, "live"), "live", "blank_dead")) %>%
  pivot_longer(cols=starts_with("met"), names_to = "metabolite") %>%
  mutate(tissue=str_extract(metabolite, "leaf|root")) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value~ trt*genotype   , data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))

```


```{r}
met_per_mg_lm_results_trt <- met_per_mg_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
  select(-`p.value_trt_(Intercept)`)

met_per_mg_lm_results_leaf <- met_per_mg_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
  select(-`p.value_leaf_(Intercept)`)

met_per_mg_lm_results <- inner_join(met_per_mg_lm_results_trt, met_per_mg_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
  group_by(tissue) %>%
  mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
  rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
  ungroup()

met_per_mg_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())

met_per_mg_lm_results %>% 
  mutate(both=(p.value_trt_trtlive < 0.1) & (p.value_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("p.value"), ~ sum(.x < .05)), both=sum(both), count=n())

met_per_mg_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)



met_per_mg_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv("../output/met_per_mg_lm_full.csv")
```

raw
```{r}
met_amt_lm <- met_amt %>%
  mutate(trt=ifelse(str_detect(trt, "live"), "live", "blank_dead")) %>%
  pivot_longer(cols=starts_with("met"), names_to = "metabolite") %>%
  mutate(tissue=str_extract(metabolite, "leaf|root")) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))

```


```{r}
met_amt_lm_results_trt <- met_amt_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
  select(-`p.value_trt_(Intercept)`)

met_amt_lm_results_leaf <- met_amt_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
  select(-`p.value_leaf_(Intercept)`)

met_amt_lm_results <- inner_join(met_amt_lm_results_trt, met_amt_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
  group_by(tissue) %>%
  mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
  rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
  ungroup()

met_amt_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())

met_amt_lm_results %>% 
  mutate(both=(p.value_trt_trtlive < 0.1) & (p.value_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("p.value"), ~ sum(.x < .05)), both=sum(both), count=n())

met_amt_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)

met_amt_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv("../output/met_amt_lm_full.csv")
```

# NO BLANK


## find sig metabolites
Live vs  dead

normalized
```{r}
met_per_mg_lm <- met_per_mg %>%
  filter(trt != "BLANK") %>% 
  mutate(trt=ifelse(str_detect(trt, "live"), "live", "blank_dead")) %>%
  pivot_longer(cols=starts_with("met"), names_to = "metabolite") %>%
  mutate(tissue=str_extract(metabolite, "leaf|root")) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))

```


```{r}
met_per_mg_lm_results_trt <- met_per_mg_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
  select(-`p.value_trt_(Intercept)`)

met_per_mg_lm_results_leaf <- met_per_mg_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
  select(-`p.value_leaf_(Intercept)`)

met_per_mg_lm_results <- inner_join(met_per_mg_lm_results_trt, met_per_mg_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
  group_by(tissue) %>%
  mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
  rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
  ungroup()

met_per_mg_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())

met_per_mg_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)

met_per_mg_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv("../output/met_per_mg_lm_full_NOBLANK.csv")
```

raw
```{r}
met_amt_lm <- met_amt %>%
  filter(trt != "BLANK") %>% 
  pivot_longer(cols=starts_with("met"), names_to = "metabolite") %>%
  mutate(tissue=str_extract(metabolite, "leaf|root")) %>%

  #scale and center
  group_by(metabolite, genotype, tissue) %>%
  mutate(value_scale=scale(value)) %>%
  group_by(metabolite) %>%
  nest() %>%
  mutate(lm_trt=map(data, ~ lm(value ~ trt * genotype, data=.)),
         lm_leaf=map(data, ~ lm(leaf_avg_std ~ value_scale, data=.)))

```


```{r}
met_amt_lm_results_trt <- met_amt_lm %>% mutate(broomtidy.trt = map(lm_trt, broom::tidy)) %>%
  unnest(broomtidy.trt) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "trt_") %>%
  select(-`p.value_trt_(Intercept)`)

met_amt_lm_results_leaf <- met_amt_lm %>% mutate(broomtidy.leaf = map(lm_leaf, broom::tidy)) %>%
  unnest(broomtidy.leaf) %>%
  select(metabolite, term, estimate, p.value) %>%
  pivot_wider(id_cols = metabolite, names_from = term, values_from = c(estimate, p.value), names_prefix = "leaf_") %>%
  select(-`p.value_leaf_(Intercept)`)

met_amt_lm_results <- inner_join(met_amt_lm_results_trt, met_amt_lm_results_leaf) %>%
  mutate(tissue=str_extract(metabolite,"(leaf|root)")) %>%
  group_by(tissue) %>%
  mutate(across(starts_with("p.value"), .fns = p.adjust, method = "fdr", .names = "FDR_{.col}")) %>%
  rename_with(~ str_replace(.x, "FDR_p.value", "FDR")) %>%
  ungroup()

met_amt_lm_results %>% 
  mutate(both=(FDR_trt_trtlive < 0.1) & (FDR_leaf_value_scale < 0.1)) %>%
  group_by(tissue) %>%
  summarize(across(starts_with("FDR"), ~ sum(.x < .1)), both=sum(both), count=n())

met_amt_lm_results %>% filter(FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>% 
  select(metabolite, FDR_trt_trtlive, FDR_leaf_value_scale)

met_amt_lm_results %>% 
  #filter(FDR.trt < 0.1 | FDR.leaf < 0.1) %>%
  mutate(sig.both = FDR_trt_trtlive < 0.1 & FDR_leaf_value_scale < 0.1) %>%
  arrange(desc(sig.both), min(.$FDR_trt_trtlive, .$FDR_leaf_value_scale)) %>% 
  write_csv("../output/met_amt_lm_full_NOBLANK.csv")
```
